The present invention relates to checkout systems, and in particular to virtualization of checkout lines (queues) in a retail establishment.
Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Queuing at checkout counters seems to be an unavoidable part in the daily shopping experience. A study by a British online discount store revealed that the average Briton spends one month queuing during his lifetime. Recent market research data indicates that in Germany on average 4.72 customers are waiting in supermarket lines, leading to an average waiting time of seven minutes.
For the majority of shoppers, queuing is perceived to be bothersome. A study reveals that 35% of shoppers strongly dislike queuing, 31% of shoppers dislike it, 21% “mind a little” and only 13% do not mind. Even more interesting is that 80% of the shoppers polled indicated that they are likely to avoid shopping during busy times, and the study gives evidence that they actually do so. Another study showed the impatience even of those usually thought to be “queue-tolerant”: 68% of the customers regularly abandon shopping if they have to wait too long. The upper waiting time limit of two-fifths of the surveyed participants is set to only two minutes. In comparison, the average waiting time in most countries is above two minutes. Remarkable also is the effect of spotting long queues by customers who enter a supermarket. In the above-mentioned study, 51% refuse to even enter the store if they spy a queue.
Related technologies and applications can be found in the domains of (i) checkout mechanisms, (ii) mobile transactions and (iii) recommender systems.
Checkout mechanisms: Methods related to the “checkout-experience” have received much attention in the past, but only few of them exploit the possibilities of mobile devices. In U.S. retail chains like Wal-Mart®, there are so-called “self check-out” counters. Here, the customer has to scan each item manually and pay at the terminal and therefore contribute individually to the system's efficiency. Other approaches aim at improving the payment efficiency. After a high enough adoption rate of near-field communication (NFC), mobile payment and the mobile wallet approaches are generally thought to be the future way. Another approach to improve checkout is fully automatic checkout, where the items are put in a smart basket, where they are recognized, for example, using radio frequency ID (RFID) technology. Connected with a mobile payment system, queuing would almost be completely avoided. Other approaches aim at converting physical to virtual queues, where customers take a ticket and wait until their number is displayed on a screen. For example this approach is deployed in the Swiss Post offices. An application for virtual queuing in recreational parks is Disney FASTPASS™, which envisions to enable mobile devices to make reservations for attractions in advance.
Mobile transactions: Traditional platforms for transactions between internet users include eBay™ or Craigslist™, which are also accessible with smart phones. Zaarly™ is a location-aware mobile market place, where anyone can publish a request for some good or service, which includes a money bid and location requirements. Although only existing as an idea so far, it is believed by some to be the future of mobile markets, or even the next big thing in mobile commerce. Although a very new area, it is apparent that mobile transactions will be a hot topic in the future.
Recommending/targeting systems: Systems that help assist customers while shopping have been researched extensively, where most solutions are store-independent. Systems like ShopSavvy™, RedLaser™ and Barcoo™ let the user scan an item, and display price comparisons and ratings. Depending on the rating or review, a product may be recommended explicitly. Other systems aim at implicitly recommend items based on the user's profile or previous behavior. Collaborative systems like GroupLens™, PHOAKS™ and Video Recommender™ find users with similar tastes and recommend what they liked in the past. A prominent example is the product recommendation provided by Amazon®. Content based systems identify items similar to those preferred in the past. They are especially suited for textual information like news. GS1™ envisions product recommendations and targeting to be integrated in an integrated retail solution.